Digital Image Processing

Abraham Varghese ¹, Balakrishnan Kannan , Joseph Paul

Affiliations
1 Anna University, IN

Abstract

Restoration of image features from local binary pattern (LBP) coded MR images form an important application in Telemedicine. LBP is invariant to monotonic gray scale change and it can be used as a rotational invariant texture descriptor. In this paper, we use co-occurrence features of local binary patterns to edge enhance low contrast MR images. It is thereby shown that the co-occurrence features are able to retrieve the anatomical information present in the original MR image, which otherwise can be obtained using a standard edge enhancing kernel. Out of 14 textural features extracted from co-occurrence matrix, the measure sum-average performs better in preserving anatomical information.

Keywords

Co-Occurrence Matrix, Edge Enhancement, Local Binary Pattern (LBP), Sum Average Haralick Feature (SAHF).

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Abraham Varghese ¹, Reji Rajan Varghese ², Balakrishnan Kannan ³, J. S. Paul ⁴

Affiliations
1 Computer Science and Engineering, ASIET, Kalady, IN
2 Co-Operative Medical College, Kochi, IN
3 CUSAT, IN
4 Indian Institute of Information Technology and Management, Trivandrum, IN

Abstract

Local Binary Pattern has been used as texture descriptor for various medical image applications due to its invariant to monotonic gray level change and easier computation. LBP may fail in the case of noisy images or on flat image areas of constant gray level due to the thresholding scheme of the operator. Local Binary Pattern with ternary encoding has been proposed to identify the region of Interest in Brain MR Images. The proper threshold has been calculated locally based on the range of pixels in each window. This will reduce the complexity of the Image retrieval problem, especially Brain Slice retrieval.

Keywords

Neighborhood Calculation, Local Binary Pattern, Ternary Encoding, Region of Interest.

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